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    Π‘Π ΠΠ’ΠΠ˜Π’Π•Π›Π¬ΠΠ«Π™ ΠΠΠΠ›Π˜Π— Π‘Π•Π—Π­Π’ΠΠ›ΠžΠΠΠ«Π₯ ΠœΠ•Π  ΠžΠ¦Π•ΠΠšΠ˜ ΠšΠΠ§Π•Π‘Π’Π’Π Π¦Π˜Π€Π ΠžΠ’Π«Π₯ Π˜Π—ΠžΠ‘Π ΠΠ–Π•ΠΠ˜Π™

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    This paper presents results of a comparative analysis of 34 measures published in the scientific literature and used for evaluation of the image quality without a reference image. In English literature, they are called no-reference (NR) measure or measures NR-type. The first article, the term no-reference, was published in 2000 and each year a growing number of publications on new measures NR-type. However, comparative studies of such measures is not practically conducted. Such measures are very important for a) just made photo quality evaluation, b) assessment of image enhancement transformations and selection of their parameters (such as contrast and brightness adjustments, tone-mapping, decolorization and others). Publicly available image quality databases used for study no-reference quality measures (TID2013, etc.), contain 4-5 variants of images distorted by predefined transformations with unknown parameters. We presented six types of experiments to analyze correlation of the computed numerical quality values with visual estimates of the test images quality. Four of the experiments are new: comparison of images after gamma-correction and contrast enhancement with different parameters, as well as analysis of the retouched images and photos taken with different focal length. It was shown experimentally that no one of the known no-reference quality assessment measure is universal, and the calculated value cannot be converted to a quality scale, excluding factors influencing the distortion of the image. Most of the studied measures calculates local estimates in small neighborhoods, and their arithmetic mean is the quality index of the image. If the image contains large areas of uniform brightness, the measures of this type can give incorrect quality assessment, which will not correlate with the visual assessments.Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ ΠΎΠΏΠΈΡΡ‹Π²Π°ΡŽΡ‚ΡΡ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΡΡ€Π°Π²Π½ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° 34 Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΉ, ΠΎΠΏΡƒΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½Π½Ρ‹Ρ… Π² Π½Π°ΡƒΡ‡Π½ΠΎΠΉ Π»ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€Π΅ ΠΈ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‰ΠΈΡ…ΡΡ для ΠΎΡ†Π΅Π½ΠΊΠΈ качСства ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΡ€ΠΈ отсутствии эталона. Π’ англоязычной Π»ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€Π΅ ΠΎΠ½ΠΈ Π½Π°Π·Ρ‹Π²Π°ΡŽΡ‚ΡΡ no-reference (NR) measure ΠΈΠ»ΠΈ ΠΌΠ΅Ρ€Π°ΠΌΠΈ NR-Ρ‚ΠΈΠΏΠ°. ΠŸΠ΅Ρ€Π²Π°Ρ ΡΡ‚Π°Ρ‚ΡŒΡ, ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‰Π°Ρ Ρ‚Π΅Ρ€ΠΌΠΈΠ½ no-reference, Π±Ρ‹Π»Π° ΠΎΠΏΡƒΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½Π° Π² 2000 Π³ΠΎΠ΄Ρƒ ΠΈ Π΅ΠΆΠ΅Π³ΠΎΠ΄Π½ΠΎ растСт число ΠΏΡƒΠ±Π»ΠΈΠΊΠ°Ρ†ΠΈΠΉ ΠΎ Π½ΠΎΠ²Ρ‹Ρ… ΠΌΠ΅Ρ€Π°Ρ… NR-Ρ‚ΠΈΠΏΠ°. Π’Π΅ΠΌ Π½Π΅ ΠΌΠ΅Π½Π΅Π΅, ΡΡ€Π°Π²Π½ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… исслСдований Ρ‚Π°ΠΊΠΈΡ… ΠΌΠ΅Ρ€ практичСски Π½Π΅ ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡŒ. НаличиС NR-ΠΌΠ΅Ρ€ ΠΎΡ‡Π΅Π½ΡŒ Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎ для, Π°) ΠΎΡ†Π΅Π½ΠΊΠΈ качСства сдСланных Ρ„ΠΎΡ‚ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΉ, Π±) ΠΎΡ†Π΅Π½ΠΊΠΈ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠΉ, ΠΎΡ€ΠΈΠ΅Π½Ρ‚ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… Π½Π° ΡƒΠ»ΡƒΡ‡ΡˆΠ΅Π½ΠΈΠ΅ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, ΠΈ Π²Ρ‹Π±ΠΎΡ€ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² этих ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠΉ (яркостныС измСнСния, сТатиС динамичСского Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° яркости, ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ Ρ†Π²Π΅Ρ‚Π½ΠΎΠ³ΠΎ Π² ΠΏΠΎΠ»ΡƒΡ‚ΠΎΠ½ ΠΈ Π΄Ρ€ΡƒΠ³ΠΈΠ΅). Π‘Π°Π·Ρ‹ тСстовых ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅ΠΌΡ‹Π΅ для исслСдования Π±Π΅Π· эталонных ΠΌΠ΅Ρ€ качСства (TID2013 ΠΈ Π΄Ρ€ΡƒΠ³ΠΈΠ΅), содСрТат ΠΏΠΎ 4, 5 Π²Π°Ρ€ΠΈΠ°Π½Ρ‚ΠΎΠ² ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ³ΠΎ Ρ‚ΠΈΠΏΠ° искаТСний ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹ ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… Π½Π΅ описаны. ΠŸΠΎΡΡ‚ΠΎΠΌΡƒ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½ΠΎ ΡˆΠ΅ΡΡ‚ΡŒ Ρ‚ΠΈΠΏΠΎΠ² экспСримСнтов с Ρ†Π΅Π»ΡŒΡŽ Π°Π½Π°Π»ΠΈΠ·Π° коррСляции вычисляСмых количСствСнных ΠΎΡ†Π΅Π½ΠΎΠΊ с Π²ΠΈΠ·ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΠΎΡ†Π΅Π½ΠΊΠ°ΠΌΠΈ качСства тСстируСмых ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ. Π§Π΅Ρ‚Ρ‹Ρ€Π΅ ΠΈΠ· Π½ΠΈΡ… ΡΠ²Π»ΡΡŽΡ‚ΡΡ ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΠΈΠ°Π»ΡŒΠ½ΠΎ Π½ΠΎΠ²Ρ‹ΠΌΠΈ: сравнСниС ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ послС Π³Π°ΠΌΠΌΠ°-ΠΊΠΎΡ€Ρ€Π΅ΠΊΡ†ΠΈΠΈ ΠΈ измСнСния контраста с Ρ€Π°Π·Π½Ρ‹ΠΌΠΈ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°ΠΌΠΈ, Π° Ρ‚Π°ΠΊΠΆΠ΅ сравнСниС ΠΎΡ‚Ρ€Π΅Ρ‚ΡƒΡˆΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ Ρ„ΠΎΡ‚ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΉ, сдСланных с Ρ€Π°Π·Π½Ρ‹ΠΌ фокусным расстояниСм. Π­ΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½ΠΎ ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ, Ρ‡Ρ‚ΠΎ Π½ΠΈ ΠΎΠ΄Π½Π° ΠΈΠ· исслСдуСмых ΠΌΠ΅Ρ€ ΠΎΡ†Π΅Π½ΠΊΠΈ качСства изобраТСния Π½Π΅ являСтся ΡƒΠ½ΠΈΠ²Π΅Ρ€ΡΠ°Π»ΡŒΠ½ΠΎΠΉ, Π° вычислСнная ΠΎΡ†Π΅Π½ΠΊΠ° Π½Π΅ ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½Π° Π² ΠΊΠ°Ρ‡Π΅ΡΡ‚Π²Π΅Π½Π½ΡƒΡŽ ΡˆΠΊΠ°Π»Ρƒ Π±Π΅Π· ΡƒΡ‡Π΅Ρ‚Π° Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° искаТСниС качСства изобраТСния. Π‘ΠΎΠ»ΡŒΡˆΠΈΠ½ΡΡ‚Π²ΠΎ исслСдованных ΠΌΠ΅Ρ€ вычисляСт Π»ΠΎΠΊΠ°Π»ΡŒΠ½Ρ‹Π΅ ΠΎΡ†Π΅Π½ΠΊΠΈ Π² ΠΌΠ°Π»Ρ‹Ρ… окрСстностях, Π° ΠΈΡ… срСднСС арифмСтичСскоС являСтся ΠΎΡ†Π΅Π½ΠΊΠΎΠΉ качСства всСго изобраТСния. Если Π½Π° ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΈ Π΄ΠΎΠΌΠΈΠ½ΠΈΡ€ΡƒΡŽΡ‚ большиС области ΠΎΠ΄Π½ΠΎΡ€ΠΎΠ΄Π½ΠΎΠΉ яркости, ΠΌΠ΅Ρ€Ρ‹ Ρ‚Π°ΠΊΠΎΠ³ΠΎ Ρ‚ΠΈΠΏΠ° ΠΌΠΎΠ³ΡƒΡ‚ Π΄Π°Ρ‚ΡŒ Π½Π΅Π²Π΅Ρ€Π½Ρ‹Π΅ ΠΎΡ†Π΅Π½ΠΊΠΈ качСства, Π½Π΅ ΡΠΎΠ²ΠΏΠ°Π΄Π°ΡŽΡ‰ΠΈΠ΅ с Π²ΠΈΠ·ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΠΎΡ†Π΅Π½ΠΊΠ°ΠΌΠΈ

    ΠŸΠΠ ΠΠœΠ•Π’Π Π« ΠšΠ Π˜Π’ΠžΠ™ Π ΠΠ‘ΠŸΠ Π•Π”Π•Π›Π•ΠΠ˜Π― Π›ΠžΠšΠΠ›Π¬ΠΠ«Π₯ ΠžΠ¦Π•ΠΠžΠš КАК ΠœΠ•Π Π« ΠšΠΠ§Π•Π‘Π’Π’Π Π˜Π—ΠžΠ‘Π ΠΠ–Π•ΠΠ˜Π™

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    This article focuses on selecting the best quality image from the series without the reference image. The results of studies of a new approach to digital image quality assessment, based on the local quality estimates distribution, are presented. One of the parameters of such a distribution is proposed to be used as a measure of image quality. 16 quality measures of the images described in the scientific literature have been selected. It is shown that the scale parameter of the Weibull distribution is a more accurate global quality measure for the set of local estimates than the mean value. A number of experiments have been carried out to confirm the correctness of such an estimate and its correlation with visual estimates of image quality. Such estimates are very important for a) quality assessment of automatically generated photographs, b) selection of parameters for enhancement-oriented image transformations, such as brightness changes, compression of the dynamic range of brightness, conversion to the grayscale representation, and others.Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ исслСдуСтся Π·Π°Π΄Π°Ρ‡Π° Π²Ρ‹Π±ΠΎΡ€Π° Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ качСствСнного изобраТСния ΠΈΠ· сСрии Π² отсутствиС эталона. ΠžΠΏΠΈΡΡ‹Π²Π°ΡŽΡ‚ΡΡ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ исслСдований Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Π° ΠΊ Ρ„ΠΎΡ€ΠΌΠΈΡ€ΠΎΠ²Π°Π½ΠΈΡŽ ΠΎΡ†Π΅Π½ΠΊΠΈ качСства Ρ†ΠΈΡ„Ρ€ΠΎΠ²Ρ‹Ρ… ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, основанного Π½Π° построСнии ΠΊΡ€ΠΈΠ²ΠΎΠΉ распрСдСлСния Π»ΠΎΠΊΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΎΡ†Π΅Π½ΠΎΠΊ качСства. Один ΠΈΠ· ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² Ρ‚Π°ΠΊΠΎΠΉ ΠΊΡ€ΠΈΠ²ΠΎΠΉ прСдлагаСтся ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ ΠΊΠ°ΠΊ ΠΌΠ΅Ρ€Ρƒ качСства ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ. ΠžΡ‚ΠΎΠ±Ρ€Π°Π½Ρ‹ 16 ΠΌΠ΅Ρ€ качСства ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, описанных Π² Π½Π°ΡƒΡ‡Π½ΠΎΠΉ Π»ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€Π΅. Показано, Ρ‡Ρ‚ΠΎ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ ΠΌΠ°ΡΡˆΡ‚Π°Π±Π° распрСдСлСния Π’Π΅ΠΉΠ±ΡƒΠ»Π»Π° являСтся Π±ΠΎΠ»Π΅Π΅ Ρ‚ΠΎΡ‡Π½ΠΎΠΉ ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Π»ΡŒΠ½ΠΎΠΉ ΠΌΠ΅Ρ€ΠΎΠΉ качСства для мноТСства Π»ΠΎΠΊΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΎΡ†Π΅Π½ΠΎΠΊ, Ρ‡Π΅ΠΌ срСднСС арифмСтичСскоС. Π’Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ ряд экспСримСнтов, ΠΏΠΎΠ΄Ρ‚Π²Π΅Ρ€ΠΆΠ΄Π°ΡŽΡ‰ΠΈΡ… ΠΊΠΎΡ€Ρ€Π΅ΠΊΡ‚Π½ΠΎΡΡ‚ΡŒ Ρ‚Π°ΠΊΠΎΠΉ ΠΎΡ†Π΅Π½ΠΊΠΈ ΠΈ Π΅Π΅ ΠΊΠΎΡ€Ρ€Π΅Π»ΡΡ†ΠΈΡŽ с Π²ΠΈΠ·ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΠΎΡ†Π΅Π½ΠΊΠ°ΠΌΠΈ качСства ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ. НаличиС ΠΏΠΎΠ΄ΠΎΠ±Π½Ρ‹Ρ… ΠΌΠ΅Ρ€ ΠΎΡ‡Π΅Π½ΡŒ Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎ для Π°) ΠΎΡ†Π΅Π½ΠΊΠΈ качСства автоматичСски Ρ„ΠΎΡ€ΠΌΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ… Ρ„ΠΎΡ‚ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΉ, Π±) Π²Ρ‹Π±ΠΎΡ€Π° ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠΉ, ΠΎΡ€ΠΈΠ΅Π½Ρ‚ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… Π½Π° ΡƒΠ»ΡƒΡ‡ΡˆΠ΅Π½ΠΈΠ΅ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, Ρ‚Π°ΠΊΠΈΡ… ΠΊΠ°ΠΊ яркостныС измСнСния, сТатиС динамичСского Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° яркости, ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ Π² ΠΏΠΎΠ»ΡƒΡ‚ΠΎΠ½ΠΎΠ²ΠΎΠ΅ прСдставлСниС ΠΈ Π΄Ρ€ΡƒΠ³ΠΈΡ…

    О Π¦Π˜Π€Π ΠžΠ’ΠžΠ™ Π Π•Π‘Π’ΠΠ’Π ΠΠ¦Π˜Π˜ Π˜Π‘Π’ΠžΠ Π˜Π§Π•Π‘ΠšΠ˜Π₯ Π’Π•ΠšΠ‘Π’ΠžΠ’Π«Π₯ Π”ΠžΠšΠ£ΠœΠ•ΠΠ’ΠžΠ’

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    The main problems of old text document restoration and approaches to their solution during restoration of images of these documents by methods of information technology are considered. Primary source documents are not changed, but their digital copies can be modified with orientation on different applications and according to the different levels of processingΠ Π°ΡΡΠΌΠ°Ρ‚Ρ€ΠΈΠ²Π°ΡŽΡ‚ΡΡ основныС ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ рСставрации старых тСкстовых Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚ΠΎΠ² ΠΈ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Ρ‹ ΠΊ ΠΈΡ… Ρ€Π΅ΡˆΠ΅Π½ΠΈΡŽ ΠΏΡ€ΠΈ рСставрации ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ этих Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚ΠΎΠ² ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌΠΈ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ. Π”ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚Ρ‹-пСрвоисточники ΠΏΡ€ΠΈ этом Π½Π΅ ΠΏΠΎΠ΄Π²Π΅Ρ€Π³Π°ΡŽΡ‚ΡΡ измСнСниям, Π° ΠΈΡ… Ρ†ΠΈΡ„Ρ€ΠΎΠ²Ρ‹Π΅ ΠΊΠΎΠΏΠΈΠΈ ΠΌΠΎΠΆΠ½ΠΎ ΠΌΠΎΠ΄ΠΈΡ„ΠΈΡ†ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ с ΠΎΡ€ΠΈΠ΅Π½Ρ‚Π°Ρ†ΠΈΠ΅ΠΉ Π½Π° Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Π΅ примСнСния ΠΈ согласно Ρ€Π°Π·Π½Ρ‹ΠΌ уровням ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ.

    Application of the Clahe Method Contrast Enhancement of X-Ray Images

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    Due to the nonlinearity of the luminance function produced by many medical recording devices, the quality of medical images deteriorates, which creates problems in the visual research work of physicians. X-rays can be taken as an example. This article examines methods of improving the contrast of graphic images methods of improving the quality of X-ray images. The research was carried out in several stages. Attempts were made to increase the contrast of several dozen X-ray images to select the best image brightness using brightness conversion methods in the MATLAB system. Contrast enhancement was observed during the experiments, resulting in the selection of a brightness range corresponding to the visual contrast enhancement. The selection of variables Ξ³ for the selected brightness range of the image was performed. The possibilities of the image histogram equalization method were considered. To obtain the best result before performing gamma correction the method of X-ray image histogram equalization is suggested. An enhancement version of this algorithm is presented because of the comparison. Application of the adaptive histogram equalization algorithm with contrast limitation provides a visual effect of improving the contrast of X-ray images. The NIQE and BRISQUE evaluation functions, which do not use reference images, are used to objectively quantify the conversion results

    Image Semantic Segmentation Based on High-Resolution Networks for Monitoring Agricultural Vegetation

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    In the article, recognition of state of agricultural vegetation from aerial photographs at various spatial resolutions was considered. Proposed approach is based on a semantic segmentation using convolutional neural networks. Two variants of High-Resolution network architecture (HRNet) are described and used. These neural networks were trained and applied to aerial images of agricultural fields. In our experiments, accuracy of four land classes recognition (soil, healthy vegetation, diseased vegetation and other objects) was about 93-94%

    Π‘Π˜ΠΠ“Π£Π›Π―Π ΠΠžΠ• Π ΠΠ—Π›ΠžΠ–Π•ΠΠ˜Π• МАВРИЦ Π’ ΠΠΠΠ›Π˜Π—Π• Π¦Π˜Π€Π ΠžΠ’Π«Π₯ Π˜Π—ΠžΠ‘Π ΠΠ–Π•ΠΠ˜Π™

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    The paper describes new properties of the singular matrix decomposition. It is shown that permutation of rows or columns of the matrix or matrix rotation by 90 degrees does not change the set of its singular numbers. However, variation the value of at least one matrix element or permutation of any two matrix elements leads to a modification of the whole set of the singular numbers. Examples of image sharpening and contrast enhancement by modification of the singular numbers are given.ΠžΠΏΠΈΡΡ‹Π²Π°ΡŽΡ‚ΡΡ Π½ΠΎΠ²Ρ‹Π΅ свойства сингулярных чисСл, вычисляСмых для ΠΌΠ°Ρ‚Ρ€ΠΈΡ† Ρ†ΠΈΡ„Ρ€ΠΎΠ²Ρ‹Ρ… ΠΈΠ·ΠΎ-Π±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ. Показано, Ρ‡Ρ‚ΠΎ пСрСстановка строк ΠΈΠ»ΠΈ столбцов ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ ΠΈ Π΅Π΅ ΠΏΠΎΠ²ΠΎΡ€ΠΎΡ‚ Π½Π° 90Β° Π½Π΅ ΠΌΠ΅Π½ΡΡŽΡ‚ мноТСства сингулярных чисСл, ΠΎΠ΄Π½Π°ΠΊΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ значСния ΠΎΠ΄Π½ΠΎΠ³ΠΎ элСмСнта ΠΈΠ»ΠΈ пСрСстановка мСстами Π΄Π²ΡƒΡ… элСмСнтов ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ привСсти ΠΊ измСнСнию всСго мноТСства сингулярных чисСл. ΠŸΡ€ΠΈΠ²ΠΎΠ΄ΡΡ‚ΡΡ ΠΏΡ€ΠΈΠΌΠ΅Ρ€Ρ‹ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡ рСзкости ΠΈ контраста ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΡƒΡ‚Π΅ΠΌ ΠΌΠΎΠ΄ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ мноТСства сингулярных чисСл
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