Analysis Of Sparse Representation Of The Hr Patch

Shi Et al [13] used a local projection profile at each pixel of the image, and transform the original image into an adaptive local connectivity map (ALCM). For this process, first the gray scale image is reversed so the foreground pixels (text) have idensity values of up to 255. Then the image is downsample to ¼ of each size, ½ in each direction. Next a sliding window of size 2c scans the image from left to right, and right to left in order to compute the cumulative idensity of every neighborhood. This technique is identical to computing the projection profile of every sliding window, (i.e. counting the foreground pixels), but instead of outpiting a projection profile histogram, the entire sliding window sum is saved on the ALCM image. Finaly

This strategy has been extensively used for inpainting and the algorithms ca be smmrizeed as follwons:

In accession to the binary images, the proposed method may be tested on discrete color images also. These type of

Regularization term [eq:piece_overlap_1] is summed up for all pieces to provide inter-piece smoothness constraint. It should be noted that this added term only ensures the the estimation discontinuities being minimized but not completely eliminated. Therefore, the estimated local deformations are interpolated in order to obtain a global deformation field at the end of each subdivision level. The final deformation field is obtained by composing deformations obtained at each subdivision level.

The proposed model combines the outputs of two simple filters: entropy and a differential standard deviation filter to estimate visual sensitivity to noise. The two outputs are mixed using a non-linear function and a smoothing low-pass filter in a post-processing step. As a result, image localities with sharp edges of arbitrary shape as well as uniformly or smoothly colored areas are distinguished as “highly sensitive to noise,” whereas areas with noisy texture are identified as “tolerant to noise.” This ability can be particularly appealing to several applications such as Compression, de-noising, or watermarking. In this paper, we focus on the latter one with an objective to create a tool that annotates images with 32 bits of meta-data. Note that we do not impose any security requirements for the watermarking technology [4].

Abstract: Bad weather, such as fog, haze significantly degrade the visibility of the scene. Haze is an atmospheric phenomenon that significantly degrades the visibility of outdoor scenes. It is due to the atmosphere particles that absorb and scatter the light. Removing haze means increases the visibility of the scene and applicable for both color and gray images. The method is a fusion based strategy that derives the inputs and weight maps only from the original degraded image. To minimize the artifacts introduce the weight maps. The information of the derived inputs to preserve the regions with good visibility, by computing three measures: luminance, chromaticity, and salience

Simple cells in the visual cortex of mammalian brains can be modeled by Gabor functions.[1][2] Thus, image analysis with Gabor filters is thought to be similar to perception in the human visual system.

Another technique is the Eigen backgrounds [6], this approach is based on an eigenvalue decomposition which is applied to the whole image. This extended spatial domain enabled the exploration of the spatial correlation and avoiding the tiling effect of block partitioning. The method has two phases that alternate with a learning phase for samples acquisition and eigenvector matrix computation and followed by classification step for foreground detection.

Because of the great interest of HDR images and their advantages, tone mapping techniques are used to display these images on LDR devices. Many researchers made different studies for evaluating the performance of tone mapping algorithms. Debevec and Malik proposed that a HDR image can be created from three or more LDR images of the same scene under different exposures. Through technological advances and the development of photograph technology, we can get access to HDR cameras which can take HDR photos and videos directly now. Therefore, there is an increasing demand for tone mapping algorithms. There are two types of tone mapping algorithm: global operators and local operators. The global tone mapping methods are simply mapped the input HDR image I^h (x,y) to an output LDR image I^l (x,y) = f (I^h (x,y)) , where f () is a global compression function which is spatially invariant, such as gamma function, linear function, histogram based function and the function adapted to tone reproduction curves. Hence, the local tone mapping methods decompose the input HDR image into multiple layers or areas, apply different compressing algorithm in different layers or areas and recombine all the layers or areas into a LDR image. Similar with Durand, Farbman which segmented the HDR image into two layer a base layer and a detail layer, while the base layer is obtained by applying an alternative edge-reserving smoothing operator and the detail layer is obtained by

The goal of this method is to find the correlated pixel within a certain disparity range that minimizes the associated error and maximizes the similarity.

Image processing is a methodology to perform some operations on an image, so as to urge an enhanced image or to extract some helpful data from it. It is treated as an area of signal processing where both the input and output signals are images. Images are portrayed as two dimensional matrix, and we are applying already having signal processing strategies to input matrix. Images processing finds applications in several fields like photography, satellite imaging, medical imaging, and image compression, just to name a few.

In many applications of digital imaging, a high quality image is required to allow human interpretation or machine perception. Image quality is defined in terms of spatial resolution, pixel resolution, temporal resolution and spectral resolution. For our application, we are interested in spatial resolution.

Over several past years, contrast image enhancement has generated across many applications like robot sensing, electronic products, fault detection, medical image analysis, etc. Thus, increasing in popularity of contrast enhancement of images has forces researchers to study their enhancement techniques and their effectiveness for the interpretability or perception of human viewers. Contrast enhancement is a

Nowadays digital cameras which is used to capture images and videos are storing it directly in digital form. But this digital data ie. images or videos are corrupted by various types of noises. It may cause due to some disturbances or may be impulse noise. To suppress noise and improve the image performances we use image processing schemes. In this paper they uses Kalman filter to remove the impulse noise. The Kalman filter is digital signal processing based filter. It estimates three states past, present and future of a system.[10] To remove noise from video sequences they utilize both temporal and spatial information. In the temporal domain, by collecting neighbouring frames based on similarities of all images, to remove noise from a video tracking sequence they given a low-rank matrix recovery phenomena. [11]

b) Secondly discover the intensity gradients of the image. The laplacian strategy scans for 0-intersections in the second-subordinate of the given input picture to discover the edges. Angle administrator that was displayed is directionally dependent, which means that the edges were turn to remain unchanged. In any case, ascertaining second subordinate is extremely delicate to noise. Before edge location, this commotion