Diffusion-based Light Field Synthesis

Light fields (LFs), conducive to comprehensive scene radiance recorded across angular dimensions, find wide applications in 3D reconstruction, virtual reality, and computational photography.However, the LF acquisition is inevitably time-consuming and resource-intensive due to the mainstream acquisition strategy involving manual capture or laborious software synthesis.Given such a challenge, we introduce LFdiff, a straightforward yet effective diffusion-based generative framework tailored for LF synthesis, which adopts only a single RGB image as input.LFdiff leverages disparity estimated by a monocular depth estimation network and incorporates two distinctive components: a novel condition scheme and a noise estimation network tailored for LF data.Specifically, we design a position-aware warping condition scheme, enhancing inter-view geometry learning via a robust conditional signal.We then propose DistgUnet, a disentanglement-based noise estimation network, to harness comprehensive LF representations.Extensive experiments demonstrate that LFdiff excels in synthesizing visually pleasing and disparity-controllable light fields with enhanced generalization capability.Additionally, comprehensive results affirm the broad applicability of the generated LF data, spanning applications like LF super-resolution and refocusing.

Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models

Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the open-world generalization has gained increasing popularity due to its practical value. However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e.g., CLIP-ViT-B/16. This paper, for the first time, explores the collaborative potential of leveraging much weaker VLMs to enhance the generalization of a robust single model. The affirmative findings motivate us to address the generalization problem from a novel perspective, i.e., ensemble of pre-trained VLMs. We introduce three customized ensemble strategies, each tailored to one specific scenario. Firstly, we introduce the zero-shot ensemble, automatically adjusting the logits of different models based on their confidence when only pre-trained VLMs are available. Furthermore, for scenarios with extra few-shot samples, we propose the training-free and tuning ensemble, offering flexibility based on the availability of computing resources. The proposed ensemble strategies are evaluated on zero-shot, base-to-new, and cross-dataset generalization, achieving new state-of-the-art performance. Notably, this work represents an initial stride toward enhancing the generalization performance of VLMs via ensemble. The code is available at https://github.com/zhiheLu/Ensemble_VLM.git.

Toward Real-World Light Field Super-Resolution

Deep learning has opened up new possibilities for light field super-resolution (SR), but existing methods trained on synthetic datasets with simple degradations (e.g., bicubic downsampling) suffer from poor performance when applied to complex real-world scenarios. To address this problem, we introduce LytroZoom, the first real-world light field SR dataset capturing paired low- and high-resolution light fields of diverse indoor and outdoor scenes using a Lytro ILLUM camera. Additionally, we propose the Omni-Frequency Projection Network (OFPNet), which decomposes the omni-frequency components and iteratively enhances them through frequency projection operations to address spatially variant degradation processes present in all frequency components. Experiments demonstrate that models trained on LytroZoom outperform those trained on synthetic datasets and are generalizable to diverse content and devices. Quantitative and qualitative evaluations verify the superiority of OFPNet. We believe this work will inspire future research in real-world light field SR.

A Dive into SAM Prior in Image Restoration

The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-quality (HQ) image from its degraded low-quality (LQ) observation. Multiple HQ solutions may correspond to an LQ input in this poorly posed problem, creating an ambiguous solution space. This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images. In spite of the pervasive use of hand-crafted and learned priors in IR, limited attention has been paid to the incorporation of knowledge from large-scale foundation models. In this paper, we for the first time leverage the prior knowledge of the state-of-the-art segment anything model (SAM) to boost the performance of existing IR networks in an parameter-efficient tuning manner. In particular, the choice of SAM is based on its robustness to image degradations, such that HQ semantic masks can be extracted from it. In order to leverage semantic priors and enhance restoration quality, we propose a lightweight SAM prior tuning (SPT) unit. This plug-and-play component allows us to effectively integrate semantic priors into existing IR networks, resulting in significant improvements in restoration quality. As the only trainable module in our method, the SPT unit has the potential to improve both efficiency and scalability. We demonstrate the effectiveness of the proposed method in enhancing a variety of methods across multiple tasks, such as image super-resolution and color image denoising.

Can SAM Boost Video Super-Resolution?

The primary challenge in video super-resolution (VSR) is to handle large motions in the input frames, which makes it difficult to accurately aggregate information from multiple frames. Existing works either adopt deformable convolutions or estimate optical flow as a prior to establish correspondences between frames for the effective alignment and fusion. However, they fail to take into account the valuable semantic information that can greatly enhance it; and flow-based methods heavily rely on the accuracy of a flow estimate model, which may not provide precise flows given two low-resolution frames. In this paper, we investigate a more robust and semantic-aware prior for enhanced VSR by utilizing the Segment Anything Model (SAM), a powerful foundational model that is less susceptible to image degradation. To use the SAM-based prior, we propose a simple yet effective module -- SAM-guidEd refinEment Module (SEEM), which can enhance both alignment and fusion procedures by the utilization of semantic information. This light-weight plug-in module is specifically designed to not only leverage the attention mechanism for the generation of semantic-aware feature but also be easily and seamlessly integrated into existing methods. Concretely, we apply our SEEM to two representative methods, EDVR and BasicVSR, resulting in consistently improved performance with minimal implementation effort, on three widely used VSR datasets: Vimeo-90K, REDS and Vid4. More importantly, we found that the proposed SEEM can advance the existing methods in an efficient tuning manner, providing increased flexibility in adjusting the balance between performance and the number of training parameters. Code will be open-source soon.

Unfolding Taylor's Approximations for Image Restoration

Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image. In practice, however, existing methods empirically construct encapsulated end-to-end mapping networks without deepening into the rationality, and neglect the intrinsic prior knowledge of restoration task. To solve the above problems, inspired by Taylor's Approximations, we unfold Taylor's Formula to construct a novel framework for image restoration. We find the main part and the derivative part of Taylor's Approximations take the same effect as the two competing goals of high-level contextualized information and spatial details of image restoration respectively. Specifically, our framework consists of two steps, correspondingly responsible for the mapping and derivative functions. The former first learns the high-level contextualized information and the later combines it with the degraded input to progressively recover local high-order spatial details. Our proposed framework is orthogonal to existing methods and thus can be easily integrated with them for further improvement, and extensive experiments demonstrate the effectiveness and scalability of our proposed framework.

EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration

Image deblurring has seen a great improvement with the development of deep neural networks. In practice, however, blurry images often suffer from additional degradations such as downscaling and compression. To address these challenges, we propose an Enhanced Deep Pyramid Network (EDPN) for blurry image restoration from multiple degradations, by fully exploiting the self- and cross-scale similarities in the degraded image.Specifically, we design two pyramid-based modules, i.e., the pyramid progressive transfer (PPT) module and the pyramid self-attention (PSA) module, as the main components of the proposed network. By taking several replicated blurry images as inputs, the PPT module transfers both self- and cross-scale similarity information from the same degraded image in a progressive manner. Then, the PSA module fuses the above transferred features for subsequent restoration using self- and spatial-attention mechanisms. Experimental results demonstrate that our method significantly outperforms existing solutions for blurry image super-resolution and blurry image deblocking. In the NTIRE 2021 Image Deblurring Challenge, EDPN achieves the best PSNR/SSIM/LPIPS scores in Track 1 (Low Resolution) and the best SSIM/LPIPS scores in Track 2 (JPEG Artifacts).