Simultaneous coding artifact reduction and sharpness enhancement for block-based compressed images and videos

Wang

et al. 2011

SIViP

Self Cite

Multifarious image enhancement algorithms have been used in different applications specifically. Still, some algorithm and modules are imperfect for practical use. When the image enhancement modules have been fixed or combined by a series of algorithm, we need to repair it as a whole part without changing the inside. This report aim to find an algorithm based on trained filters to repair low-quality image enhancement modules. A brief review on basic image enhancement techniques and pixel classification methods will be presented, and the procedure of trained filter will be described step by step. The experiments and results comparisons for this algorithm will be described in detail.

Section: The Classification Methods Used In This Projectmentioning

confidence: 99%

Section: Classification On Basis Of Local Complexitymentioning

confidence: 99%

See 1 more Smart Citation

Repairing imperfect video enhancement algorithms using classification-based trained filters

Shao

Wang

et al. 2011

SIViP

Self Cite

“…The local entropy of a region can be defined as follows: (5) where indicates the bin index, is the probability of pixels having a value in the range of bin and is a local region inside which the entropy is calculated. Another activity measure called Mean Absolute Difference (MAG) was presented by Shao [14] for determining the complexity of a region. MAG is defined as follows: (6) where denotes the intensity value of a pixel in a region, is the intensity of the pixel in the center, and is the number of pixels in the region.…”

Section: Pixel Classificationmentioning

confidence: 99%

An Overview and Performance Evaluation of Classification-Based Least Squares Trained Filters

Shao

IEEE Trans. on Image Process.

Haan

2008

An overview of the classification-based least squares trained filters on picture quality improvement algorithms is presented. For each algorithm, the training process is unique and individually selected classification methods are proposed. Objective evaluation is carried out to single out the optimal classification method for each application. To optimize combined video processing algorithms, integrated solutions are benchmarked against cascaded filters. The results show that the performance of integrated designs is superior to that of cascaded filters when the combined applications have conflicting demands in the frequency spectrum.

“…divided the input into different illumination parts and processed each part separately. Shao studied simultaneous coding artifact reduction and sharpness enhancement for block‐based compressed images and videos. The other approaches include the discrete cosine transform (DCT) based method and the HSV‐based infrared multi‐scale Retinex (IMSR) method .…”

Section: Introductionmentioning

confidence: 99%

Fast image enhancement based on color space fusion

Xiao

Peng

Color Research & Application

et al. 2014

The current Retinex algorithm processes the RGB channels separately for color image enhancement. However, it changes the ratios of RGB components and also causes some serious problems, such as color distortion, color noise, and the halo artifacts. To solve these issues, we propose a novel algorithm based on color space fusion. The single scale Retinex with fast mean filtering is applied to the luminance component in hue‐saturation‐value (HSV) color space. An enhancement adjustment factor is introduced to avoid color distortion and noise amplification. Then, the surrounding function is replaced by a small scale Gaussian filter in RGB color space to eliminate the halo artifact. A parameter is involved to keep the color natural when the reflection is estimated. Finally, the enhanced color image is constructed from the weighted averaging results of these two steps. The subjective and objective evaluations of many different backlight images captured by different cameras are implemented to verify the validity of the proposed algorithm in our experiments. The experimental results show that the proposed algorithm can not only significantly suppress the halo effect and noise amplification, but can also remove color distortion. Our proposed algorithm is superior to the multi‐scale Retinex with color restoration approach and other state‐of‐the‐art methods. © 2014 Wiley Periodicals, Inc. Col Res Appl, 41, 22–31, 2016