Image Quality Improvement Using Image Processing Method Image Brightness Contrast and Image Sharpening

Rizani, Fitri

doi:10.47002/mst.v1i1.200

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The network consists of two sub network modules, enhancement blocks and feature purification blocks. Two sub network blocks enhance the super resolution performance of the network by extracting complementary low-frequency features; the enhancement block gathers several highfrequency characteristics via residual operation and sub-pixel convolution; the feature purification block uses multiple stacked convolutions to refine high-frequency features [23][24]. The process of extracting complementary low-frequency features from two sub network blocks of DSRCNN model is as follows: www.ijacsa.thesai.org…”

Section: B Improvement Of Image Definition Enhancement Model Based On...mentioning

confidence: 99%

Research on Image Sharpness Enhancement Technology based on Depth Learning

Lan¹,

Che²

2023

IJACSA

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Image technology is widely used in security, traffic, monitoring, and other social activities. However, these images carrying detailed information will have feature distortion due to various external physical factors in social ingestion, transmission, and storage, resulting in poor image quality and clarity. Resolution determines the definition of an image. Superresolution reconstruction is the process of transforming a lowresolution picture into a high-resolution image. To enhance the image clarity, this experiment introduces the advantages and disadvantages of the Super Resolution Convolutional Network (SRCNN) and Fast Super Resolution Convolutional Neural Network (FSRCNN) model and then constructs an image super resolution method based on DSRCNN. The algorithm consists of two sub-network blocks, an enhancement block and a purification block. The model first uses two Convolutional Neural Networks (CNN) to obtain complementary low-frequency information that improves the model's learning ability; next, it employs an enhancement block to fuse the image features of two paths via residual operation and sub-pixel convolution to prevent the loss of low-resolution image information; finally, it employs a feature purification block to refine high-frequency information that more accurately represents the predicted high-quality image. It is found that the PSNR and SSIM of the DSRCNN model can reach 33.43dB and 0.9157dB, respectively.

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Section: B Improvement Of Image Definition Enhancement Model Based On...mentioning

confidence: 99%

Research on Image Sharpness Enhancement Technology based on Depth Learning

Lan¹,

Che²

2023

IJACSA

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“…Some of the techniques used in image processing are as follows [9][10][11]. Brightness [12][13][14] is a process for adjusting the brightness of an image. If the pixel intensity is reduced by a certain value, the image will be darker and vice versa, if the pixel intensity is increased by a certain value, the image will be lighter.…”

Section: Introduction a Backgroundmentioning

confidence: 99%

Retinal Analysis and Calculating Cup to Disc Ratio Value Using Deep Learning Models

Nayak¹

2022

IJRASET

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Glaucoma is a disease of the retina caused by high intraocular pressure. The intraocular pressure in people with glaucoma can reach 60-70 mm Hg. This disease is characterized by an increasing cup to disc ratio size. Glaucoma has three levels, namely mild with a cup to disc ratio value of 0.3-0.5, moderate with a cup to disc ratio value of 0.5-0.7 and severe with a cup to disc ratio value above 0.7. For retinal analysis and calculating the cup to disc ratio value taken from a fundus camera, it must be done by an expert ophthalmologist, but it takes a long time. Therefore, feature detection and automatic cup to disc ratio value calculation are expected to assist doctors in analyzing glaucoma. The data used were 132 retinal fundus images consisting of 66 mild glaucoma images, 26 moderate glaucoma images and 40 severe glaucoma images taken from the RIM-ONE dataset (http://medimrg.webs.ull.es). Pre-processing techniques like cropping, resizing, brightness, Median Filter are used for noise removal. Subsequently, feature extraction with the help of GLCM. Consequently, the method used to classify the degree of glaucoma is the Deep Belief Network. The test simulation results obtained accuracy value of 99% with 99% of precision and

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Image Quality Improvement Using Image Processing Method Image Brightness Contrast and Image Sharpening

Cited by 2 publications

References 6 publications

Research on Image Sharpness Enhancement Technology based on Depth Learning

Research on Image Sharpness Enhancement Technology based on Depth Learning

Retinal Analysis and Calculating Cup to Disc Ratio Value Using Deep Learning Models

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