65 research outputs found
Combining Super Resolution Algorithm (Gaussian Denoising and Kernel Blurring) and Compare with Camera Super Resolution
This problem addresses the problem of low-resolution image (noisy) that will proof later by PSNR number. The best way to improve this low-resolution problem is by utilizing Super Resolution (SR) algorithm methodology. SR algorithm methodology refers to the process of obtaining higher-resolution images from several lower-resolution ones, that is resolution enhancement. The quality improvement is caused by fractional-pixel displacements between images. SR allows overcoming the limitations of the imaging system (resolving limit of the sensors) without the need for additional hardware. This research aims to find the best SR algorithm in form of stand-alone algorithm or combine algorithm by comparing with the latest SR algorithm (Camera SR) from the previous research made by Chang Chen et al in 2019. Furthermore, we confidence this research will become the future guideline for anyone who want to improve the limitation of their low-resolution camera or vision sensor by implementing those SR algorithms
A method for optimal linear super-resolution image restoration
Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ ΡΠ²Π΅ΡΡ
ΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΡ (ΠΈΠ·ΠΌΠ΅Π»ΡΡΠ΅Π½ΠΈΡ ΡΠ΅ΡΠΊΠΈ ΠΏΠΈΠΊΡΠ΅Π»ΠΎΠ²) ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ ΠΊ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠΌΡ ΡΠΈΠ³Π½Π°Π»Ρ, Π΄ΠΎΠΏΠΎΠ»Π½Π΅Π½Π½ΠΎΠΌΡ Π½ΡΠ»ΡΠΌΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΠΎΡΡΡΠ΅ΡΠ°ΠΌΠΈ (ΠΏΠΈΠΊΡΠ΅Π»Π°ΠΌΠΈ). ΠΠ»Ρ ΡΠΈΠ½ΡΠ΅Π·Π° Π²ΠΎΡΡΡΠ°Π½Π°Π²Π»ΠΈΠ²Π°ΡΡΠ΅ΠΉ ΡΠΈΡΡΠ΅ΠΌΡ Π²Π²ΠΎΠ΄ΠΈΡΡΡ Π² ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΠ΅ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎ-Π΄ΠΈΡΠΊΡΠ΅ΡΠ½Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½Π°Ρ Π΄Π»Ρ ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΈΠ·Π½Π°ΡΠ°Π»ΡΠ½ΠΎ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΡΠΉ ΡΠΈΠ³Π½Π°Π» ΡΠ½Π°ΡΠ°Π»Π° ΠΏΡΠ΅ΡΠ΅ΡΠΏΠ΅Π²Π°Π΅Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΡΠ΅ (Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅) ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΡ, Π° Π·Π°ΡΠ΅ΠΌ ΠΏΠΎΠ΄Π²Π΅ΡΠ³Π°Π΅ΡΡΡ Π΄ΠΈΡΠΊΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΈ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ Π°Π΄Π΄ΠΈΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΡΠΌΠ°. ΠΠ»Ρ ΡΠ°ΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ ΡΡΡΠΎΠΈΡΡΡ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎ ΠΊΡΠΈΡΠ΅ΡΠΈΡ ΡΡΠ΅Π΄Π½Π΅ΠΊΠ²Π°Π΄ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎ-Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π±ΠΎΠ»Π΅Π΅ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎ ΠΎΠΏΠΈΡΠ°ΡΡ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΡΠ΅Π½ΠΈΡΡ ΠΎΡΡΠ°ΡΠΎΡΠ½ΡΡ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎΡΡΡ ΡΠ°ΠΊΠΎΠ³ΠΎ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ, ΡΡΠΎ ΠΏΠΎΠ»Π΅Π·Π½ΠΎ Π΄Π»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠ΄Π° Π΄ΡΡΠ³ΠΈΡ
Π·Π°Π΄Π°Ρ (Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ). Π ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ°ΡΡΠΈ ΡΡΠ°ΡΡΠΈ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΠΎΠ±ΡΠ°Ρ ΡΡ
Π΅ΠΌΠ° Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ ΡΠ²Π΅ΡΡ
ΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΠΈΠ³Π½Π°Π»Π°, Π²ΡΠ²ΠΎΠ΄ΡΡΡΡ Π²ΡΡΠ°ΠΆΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΠΎΠΉ ΠΈ ΡΠ°ΡΡΠΎΡΠ½ΠΎΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ Π²ΠΎΡΡΡΠ°Π½Π°Π²Π»ΠΈΠ²Π°ΡΡΠ΅ΠΉ ΡΠΈΡΡΠ΅ΠΌΡ, Π° ΡΠ°ΠΊΠΆΠ΅ Π΄Π»Ρ ΠΎΡΠΈΠ±ΠΊΠΈ ΡΠ°ΠΊΠΎΠ³ΠΎ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ. ΠΠ»Ρ ΠΊΡΠ°ΡΠΊΠΎΡΡΠΈ ΠΈΠ·Π»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° Π²ΡΡ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π²Π΅Π΄Π΅ΡΡΡ Π΄Π»Ρ ΠΎΠ΄Π½ΠΎΠΌΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°, Π½ΠΎ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°ΡΡ Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½ΠΈΠ΅ Π½Π° ΡΠ»ΡΡΠ°ΠΉ Π΄Π²ΡΠΌΠ΅ΡΠ½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ. Π Π°ΡΡΠ΅ΡΠ½ΡΠΉ ΠΏΠ°ΡΠ°Π³ΡΠ°Ρ ΡΡΠ°ΡΡΠΈ ΠΏΠΎΡΠ²ΡΡΠ΅Π½ Π°Π½Π°Π»ΠΈΠ·Ρ ΠΎΡΠΈΠ±ΠΊΠΈ ΡΠ²Π΅ΡΡ
ΡΠ°Π·ΡΠ΅ΡΠ°ΡΡΠ΅Π³ΠΎ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΌΠΎΠ΄Π΅Π»ΠΈ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ. ΠΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π½ΠΎ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ ΠΏΡΠ΅Π²ΠΎΡΡ
ΠΎΠ΄ΡΡΠ²ΠΎ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΏΠΎ ΡΠΎΡΠ½ΠΎΡΡΠΈ Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΡΠΏΠΎΠ»ΡΡΠΈΠ΅ΠΉ, ΠΎΠ±ΡΡΠ½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΠΎΠΉ ΠΏΡΠΈ ΠΈΠ·ΠΌΠ΅Π»ΡΡΠ΅Π½ΠΈΠΈ ΡΠ΅ΡΠΊΠΈ ΠΏΠΈΠΊΡΠ΅Π»ΠΎΠ² ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ.ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΏΡΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ Π Π€Π€Π Π² ΡΠ°ΠΌΠΊΠ°Ρ
Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° β 19-31-90113 Π² ΡΠ°ΡΡΡΡ
Β«ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅Β», Β«ΠΠ±ΡΠ°Ρ ΡΡ
Π΅ΠΌΠ° Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ ΡΠ²Π΅ΡΡ
ΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΠΈΠ³Π½Π°Π»Π°Β», Β«ΠΠ΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎ-Π΄ΠΈΡΠΊΡΠ΅ΡΠ½Π°Ρ Π»ΠΈΠ½Π΅ΠΉΠ½Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ ΡΠΈΠ³Π½Π°Π»Π°Β», Β«ΠΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ΅ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°Β», Β«ΠΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ΅ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π° β Π°Π½Π°Π»ΠΈΠ· Π² ΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈΒ», Β«ΠΡΠΈΠ±ΠΊΠ° ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡΒ», Β«ΠΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ΅ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»Π½ΠΎΠ³ΠΎ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°Β», ΠΏΡΠΎΠ΅ΠΊΡΠ° β 19-07-00474 Π² ΡΠ°ΡΡΠΈ Β«ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π°Β»
A Neural Enhancement Post-Processor with a Dynamic AV1 Encoder Configuration Strategy for CLIC 2024
At practical streaming bitrates, traditional video compression pipelines
frequently lead to visible artifacts that degrade perceptual quality. This
submission couples the effectiveness of a neural post-processor with a
different dynamic optimsation strategy for achieving an improved
bitrate/quality compromise. The neural post-processor is refined via
adversarial training and employs perceptual loss functions. By optimising the
post-processor and encoder directly our method demonstrates significant
improvement in video fidelity. The neural post-processor achieves substantial
VMAF score increases of +6.72 and +1.81 at bitrates of 50 kb/s and 500 kb/s
respectively
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
In recent years, endomicroscopy has become increasingly used for diagnostic
purposes and interventional guidance. It can provide intraoperative aids for
real-time tissue characterization and can help to perform visual investigations
aimed for example to discover epithelial cancers. Due to physical constraints
on the acquisition process, endomicroscopy images, still today have a low
number of informative pixels which hampers their quality. Post-processing
techniques, such as Super-Resolution (SR), are a potential solution to increase
the quality of these images. SR techniques are often supervised, requiring
aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to
train a model. However, in our domain, the lack of HR images hinders the
collection of such pairs and makes supervised training unsuitable. For this
reason, we propose an unsupervised SR framework based on an adversarial deep
neural network with a physically-inspired cycle consistency, designed to impose
some acquisition properties on the super-resolved images. Our framework can
exploit HR images, regardless of the domain where they are coming from, to
transfer the quality of the HR images to the initial LR images. This property
can be particularly useful in all situations where pairs of LR/HR are not
available during the training. Our quantitative analysis, validated using a
database of 238 endomicroscopy video sequences from 143 patients, shows the
ability of the pipeline to produce convincing super-resolved images. A Mean
Opinion Score (MOS) study also confirms this quantitative image quality
assessment.Comment: Accepted for publication on Medical Image Analysis journa
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