Correcting Multi-focus Images via Simple Standard Deviation for Image Fusion

Image fusion is one of the recent trends in image registration which is an essential field of image processing. The basic principle of this paper is to fuse multi-focus images using simple statistical standard deviation. Firstly, the simple standard deviation for the k-by-k window inside each of the multi-focus images was computed. The contribution in this paper came from the idea that the focused part inside an image had high details rather than the unfocused part. Hence, the dispersion between pixels inside the focused part is higher than the dispersion inside the unfocused part. Secondly, a simple comparison between the standard deviation for each k-by-k window in the multi-focus images could be computed. The highest standard deviation between all the computed standard deviations for the multi-focus images could be treated as the optimal that is to be placed in the fused image. The experimental visual results show that the proposed method produces very satisfactory results in spite of its simplicity.

A Novel Robust Method to Add Watermarks to Bitmap Images by Fading Technique

Digital water marking is one of the essential fields in image security and copyright protection. The proposed technique in this paper was based on the principle of protecting images by hide an invisible watermark in the image. The technique starts with merging the cover image and the watermark image with suitable ratios, i.e., 99% from the cover image will be merged with 1% from the watermark image. Technically, the fading process is irreversible but with the proposed technique, the probability to reconstruct the original watermark image is great. There is no perceptible difference between the original and watermarked image by human eye. The experimental results show that the proposed technique proven its ability to hide images that have the same size of the cover image. Three performance measures were implemented to support the proposed techniques which are MSE, PSNR, and SSIM. Fortunately, all the three measures have excellent values.

Increasing Compression Ratio in PNG Images by k-Modulus Method for Image Transformation

Image compression is an important filed in image processing. The science welcomes any tinny contribution that may increase the compression ratio by whichever insignificant percentage. Therefore, the essential contribution in this paper is to increase the compression ratio for the well known Portable Network Graphics (PNG) image file format. The contribution starts with converting the original PNG image into k-Modulus Method (k-MM). Practically, taking k equals to ten, and then the pixels in the constructed image will be integers divisible by ten. Since PNG uses Lempel-Ziv compression algorithm, then the ability to reduce file size will increase according to the repetition in pixels in each k-by-k window according to the transformation done by k-MM. Experimental results show that the proposed technique (k-PNG) produces high compression ratio with smaller file size in comparison to the original PNG file.

Image Inpainting by Kriging Interpolation Technique

Image inpainting is the art of predicting damaged regions of an image. The manual way of image inpainting is a time consuming. Therefore, there must be an automatic digital method for image inpainting that recovers the image from the damaged regions. In this paper, a novel statistical image inpainting algorithm based on Kriging interpolation technique was proposed. Kriging technique automatically fills the damaged region in an image using the information available from its surrounding regions in such away that it uses the spatial correlation structure of points inside the k-by-k block. Kriging has the ability to face the challenge of keeping the structure and texture information as the size of damaged region heighten. Experimental results showed that, Kriging has a high PSNR value when recovering a variety of test images from scratches and text as damaged regions.

Image Compression By Embedding Five Modulus Method Into JPEG

The standard JPEG format is almost the optimum format in image compression. The compression ratio in JPEG sometimes reaches 30:1. The compression ratio of JPEG could be increased by embedding the Five Modulus Method (FMM) into the JPEG algorithm. The novel algorithm gives twice the time as the standard JPEG algorithm or more. The novel algorithm was called FJPEG (Five-JPEG). The quality of the reconstructed image after compression is approximately approaches the JPEG. Standard test images have been used to support and implement the suggested idea in this paper and the error metrics have been computed and compared with JPEG.

k-Modulus Method for Image Transformation

In this paper, we propose a new algorithm to make a novel spatial image transformation. The proposed approach aims to reduce the bit depth used for image storage. The basic technique for the proposed transformation is based of the modulus operator. The goal is to transform the whole image into multiples of predefined integer. The division of the whole image by that integer will guarantee that the new image surely less in size from the original image. The k-Modulus Method could not be used as a stand alone transform for image compression because of its high compression ratio. It could be used as a scheme embedded in other image processing fields especially compression. According to its high PSNR value, it could be amalgamated with other methods to facilitate the redundancy criterion.

Semi-Optimal Edge Detector based on Simple Standard Deviation with Adjusted Thresholding

This paper proposes a novel method which combines both median filter and simple standard deviation to accomplish an excellent edge detector for image processing. First of all, a denoising process must be applied on the grey scale image using median filter to identify pixels which are likely to be contaminated by noise. The benefit of this step is to smooth the image and get rid of the noisy pixels. After that, the simple statistical standard deviation could be computed for each 2X2 window size. If the value of the standard deviation inside the 2X2 window size is greater than a predefined threshold, then the upper left pixel in the 2?2 window represents an edge. The visual differences between the proposed edge detector and the standard known edge detectors have been shown to support the contribution in this paper.

Hiding Image in Image by Five Modulus Method for Image Steganography

This paper is to create a practical steganographic implementation to hide color image (stego) inside another color image (cover). The proposed technique uses Five Modulus Method to convert the whole pixels within both the cover and the stego images into multiples of five. Since each pixels inside the stego image is divisible by five then the whole stego image could be divided by five to get new range of pixels 0..51. Basically, the reminder of each number that is not divisible by five is either 1,2,3 or 4 when divided by 5. Subsequently, then a 4-by-4 window size has been implemented to accommodate the proposed technique. For each 4-by-4 window inside the cover image, a number from 1 to 4 could be embedded secretly from the stego image. The previous discussion must be applied separately for each of the R, G, and B arrays. Moreover, a stego-key could be combined with the proposed algorithm to make it difficult for any adversary to extract the secret image from the cover image. Based on the PSNR value, the extracted stego image has high PSNR value. Hence this new steganography algorithm is very efficient to hide color images.

Five Modulus Method For Image Compression

Data is compressed by reducing its redundancy, but this also makes the data less reliable, more prone to errors. In this paper a novel approach of image compression based on a new method that has been created for image compression which is called Five Modulus Method (FMM). The new method consists of converting each pixel value in an 8-by-8 block into a multiple of 5 for each of the R, G and B arrays. After that, the new values could be divided by 5 to get new values which are 6-bit length for each pixel and it is less in storage space than the original value which is 8-bits. Also, a new protocol for compression of the new values as a stream of bits has been presented that gives the opportunity to store and transfer the new compressed image easily.