scholarly journals Contrast enhancement by multi-level histogram shape segmentation with adaptive detail enhancement for noise suppression

2019 ◽  
Vol 71 ◽  
pp. 45-55 ◽  
Author(s):  
Damian Tohl ◽  
Jim S. Jimmy Li
Keyword(s):  
Contrast Enhancement ◽  
Noise Suppression ◽  
Shape Segmentation ◽  
Multi Level ◽  
Detail Enhancement ◽  
Histogram Shape

scholarly journals Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram

2019 ◽  
Vol 11 (11) ◽  
pp. 1381 ◽  
Author(s):  
Chengwei Liu ◽  
Xiubao Sui ◽  
Xiaodong Kuang ◽  
Yuan Liu ◽  
Guohua Gu ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Noise Suppression ◽  
Mapping Function ◽  
Visual Quality ◽  
Histogram Equalization ◽  
The Other ◽  
Local Contrast ◽  
Infrared Images ◽  
Local Contrast Enhancement ◽  
Block Based

In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.

scholarly journals Noise Suppression and Contrast Enhancement via Bayesian Residual Transform (BRT) in Low-Light Conditions

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Audrey G. Chung ◽  
Alexander Wong
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Noise Suppression ◽  
State Of The Art ◽  
Light Conditions ◽  
Low Light ◽  
Preliminary Results ◽  
Denoised Image

Very low-light conditions are problematic for current robotic visionalgorithms as captured images are subject to high levels of ISOnoise. We propose a Bayesian Residual Transform (BRT) model forjoint noise suppression and image enhancement for images capturedunder these low-light conditions via a Bayesian-based multiscaleimage decomposition. The BRT models a given image as thesum of residual images, and the denoised image is reconstructedusing a weighted summation of these residual images. We evaluatethe efficacy of the proposed BRT model using the VIP-LowLightdataset, and preliminary results show a notable visual improvementover state-of-the-art denoising methods.

scholarly journals Noise Suppression Using Amplitude-Shaping OCDM With 16-Chip Multi-Level Phase-Shifted En/Decoder

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 50096-50104
Author(s):  
Rachata Maneekut ◽  
Pasu Kaewplung
Keyword(s):  
Noise Suppression ◽  
Multi Level

Detail Enhancement for High Dynamic Range Images Rendering Based on Multi-Level Decomposition

2014 ◽  
Vol 543-547 ◽  
pp. 2600-2604
Author(s):  
Hua Wang ◽  
Hui Zhang ◽  
Jian Zhong Cao ◽  
Zuo Feng Zhou ◽  
Lei Yang ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Bilateral Filter ◽  
High Dynamic Range ◽  
Computational Photography ◽  
Base Layer ◽  
Range Images ◽  
Tone Reproduction ◽  
High Dynamic ◽  
Multi Level ◽  
Detail Enhancement

Various tone reproduction operators have been proposed to display high dynamic range images on low dynamic range (LDR) devices. Many recent computational photography techniques decompose an image into a piecewise smooth base layer, containing large scale variations in intensity, and a residual detail layer capturing the smaller scale details in the image. In these techniques, its important to control the scale of the extracted details and it is often needed to manipulate details in order to avoid the appearance of visual artifacts. In this paper, a new method is proposed to preserve details for high dynamic range images tone reproduction using multi-level image decomposition. We show that current base-detail decomposition techniques, based on the bilateral filter, are limited in their ability to extract detail at arbitrary scales. Thus, we achieve detail enhancement by applying the bilateral filter iteratively, and this process is called multi-level decomposition. By minimizing the proposed energy function, we can choose the proper decomposition level. Simulation results demonstrate that the proposed method can acquire better visual quality in detail enhancement while make the base smoothness.

scholarly journals An Efficient multi-level Pre-processing Algorithm for the Enhancement of Dermoscopy images in Melanoma detection

2021 ◽  
Author(s):  
JEBA DERWIN D ◽  
JEBA SINGH O ◽  
PRIESTLY SHAN B
Keyword(s):  
Skin Lesion ◽  
Contrast Enhancement ◽  
Extreme Learning Machine ◽  
Positive Impact ◽  
Lesion Detection ◽  
Phase Congruency ◽  
Lesion Segmentation ◽  
Multi Level ◽  
Learning Machine ◽  
Structural Preservation

Abstract In this paper, a multi-level algorithm for Pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal and virtual shaving before the skin lesion segmentation. The NLM filter with lowest BRISQUE score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the de-noised image. RICE algorithm is used for contrast enhancement, produces enhanced images with better structural preservation and negligible loss of information. Unsharp Masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase-congruency based method in virtual shaving shows high similarity with groundtruth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework shows that, the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information and elimination of artifacts in melanoma images. Output of proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results it is found that, the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.

An Energy Analyser for Contrast Enhancement in STEM

1974 ◽  
Vol 32 ◽  
pp. 434-435
Author(s):  
H.T. Pearce-Percy
Keyword(s):  
Contrast Enhancement ◽  
Bending Angle ◽  
Magnetic Sector ◽  
Conventional Design ◽  
Bending Radius ◽  
Sector Type

Recently an energy analyser of the uniform magnetic sector type has been installd in a 100KV microscope. This microscope can be used in the STEM mode. The sector is of conventional design (Fig. 1). The bending angle was chosen to be 90° for ease of construction. The bending radius (ρ) is 20 cm. and the object and image distances are 42.5 cm. and 30.0 cm. respectively.

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