3,174 research outputs found
Π‘Π ΠΠΠΠΠ’ΠΠΠ¬ΠΠ«Π ΠΠΠΠΠΠ ΠΠΠΠΠ’ΠΠΠΠΠΠ«Π₯ ΠΠΠ ΠΠ¦ΠΠΠΠ ΠΠΠ§ΠΠ‘Π’ΠΠ Π¦ΠΠ€Π ΠΠΠ«Π₯ ΠΠΠΠΠ ΠΠΠΠΠΠ
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 ΡΡΠ°ΡΡΠ΅ ΠΎΠΏΠΈΡΡΠ²Π°ΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° 34 ΡΡΠ½ΠΊΡΠΈΠΉ, ΠΎΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½Π½ΡΡ
Π² Π½Π°ΡΡΠ½ΠΎΠΉ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ΅ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠΈΡ
ΡΡ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ ΠΎΡΡΡΡΡΡΠ²ΠΈΠΈ ΡΡΠ°Π»ΠΎΠ½Π°. Π Π°Π½Π³Π»ΠΎΡΠ·ΡΡΠ½ΠΎΠΉ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ΅ ΠΎΠ½ΠΈ Π½Π°Π·ΡΠ²Π°ΡΡΡΡ no-reference (NR) measure ΠΈΠ»ΠΈ ΠΌΠ΅ΡΠ°ΠΌΠΈ NR-ΡΠΈΠΏΠ°. ΠΠ΅ΡΠ²Π°Ρ ΡΡΠ°ΡΡΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠ°Ρ ΡΠ΅ΡΠΌΠΈΠ½ no-reference, Π±ΡΠ»Π° ΠΎΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½Π° Π² 2000 Π³ΠΎΠ΄Ρ ΠΈ Π΅ΠΆΠ΅Π³ΠΎΠ΄Π½ΠΎ ΡΠ°ΡΡΠ΅Ρ ΡΠΈΡΠ»ΠΎ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΎ Π½ΠΎΠ²ΡΡ
ΠΌΠ΅ΡΠ°Ρ
NR-ΡΠΈΠΏΠ°. Π’Π΅ΠΌ Π½Π΅ ΠΌΠ΅Π½Π΅Π΅, ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠ°ΠΊΠΈΡ
ΠΌΠ΅Ρ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ. ΠΠ°Π»ΠΈΡΠΈΠ΅ NR-ΠΌΠ΅Ρ ΠΎΡΠ΅Π½Ρ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎ Π΄Π»Ρ, Π°) ΠΎΡΠ΅Π½ΠΊΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ΄Π΅Π»Π°Π½Π½ΡΡ
ΡΠΎΡΠΎΠ³ΡΠ°ΡΠΈΠΉ, Π±) ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ, ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΈ Π²ΡΠ±ΠΎΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΡΠΈΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ (ΡΡΠΊΠΎΡΡΠ½ΡΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ, ΡΠΆΠ°ΡΠΈΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° ΡΡΠΊΠΎΡΡΠΈ, ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ²Π΅ΡΠ½ΠΎΠ³ΠΎ Π² ΠΏΠΎΠ»ΡΡΠΎΠ½ ΠΈ Π΄ΡΡΠ³ΠΈΠ΅). ΠΠ°Π·Ρ ΡΠ΅ΡΡΠΎΠ²ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠ΅ Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±Π΅Π· ΡΡΠ°Π»ΠΎΠ½Π½ΡΡ
ΠΌΠ΅Ρ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° (TID2013 ΠΈ Π΄ΡΡΠ³ΠΈΠ΅), ΡΠΎΠ΄Π΅ΡΠΆΠ°Ρ ΠΏΠΎ 4, 5 Π²Π°ΡΠΈΠ°Π½ΡΠΎΠ² ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ ΠΊΠΎΡΠΎΡΡΡ
Π½Π΅ ΠΎΠΏΠΈΡΠ°Π½Ρ. ΠΠΎΡΡΠΎΠΌΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½ΠΎ ΡΠ΅ΡΡΡ ΡΠΈΠΏΠΎΠ² ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ² Ρ ΡΠ΅Π»ΡΡ Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ Π²ΡΡΠΈΡΠ»ΡΠ΅ΠΌΡΡ
ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ Ρ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΠΎΡΠ΅Π½ΠΊΠ°ΠΌΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ΅ΡΡΠΈΡΡΠ΅ΠΌΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ. Π§Π΅ΡΡΡΠ΅ ΠΈΠ· Π½ΠΈΡ
ΡΠ²Π»ΡΡΡΡΡ ΠΏΡΠΈΠ½ΡΠΈΠΏΠΈΠ°Π»ΡΠ½ΠΎ Π½ΠΎΠ²ΡΠΌΠΈ: ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΠΎΡΠ»Π΅ Π³Π°ΠΌΠΌΠ°-ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΠΈ ΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΊΠΎΠ½ΡΡΠ°ΡΡΠ° Ρ ΡΠ°Π·Π½ΡΠΌΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΠΎΡΡΠ΅ΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ ΡΠΎΡΠΎΠ³ΡΠ°ΡΠΈΠΉ, ΡΠ΄Π΅Π»Π°Π½Π½ΡΡ
Ρ ΡΠ°Π·Π½ΡΠΌ ΡΠΎΠΊΡΡΠ½ΡΠΌ ΡΠ°ΡΡΡΠΎΡΠ½ΠΈΠ΅ΠΌ. ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎ ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ Π½ΠΈ ΠΎΠ΄Π½Π° ΠΈΠ· ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΡ
ΠΌΠ΅Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π½Π΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½ΠΎΠΉ, Π° Π²ΡΡΠΈΡΠ»Π΅Π½Π½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° Π½Π΅ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½Π° Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ ΡΠΊΠ°Π»Ρ Π±Π΅Π· ΡΡΠ΅ΡΠ° ΡΠ°ΠΊΡΠΎΡΠΎΠ², Π²Π»ΠΈΡΡΡΠΈΡ
Π½Π° ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ. ΠΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Π½ΡΡ
ΠΌΠ΅Ρ Π²ΡΡΠΈΡΠ»ΡΠ΅Ρ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΠ΅ ΠΎΡΠ΅Π½ΠΊΠΈ Π² ΠΌΠ°Π»ΡΡ
ΠΎΠΊΡΠ΅ΡΡΠ½ΠΎΡΡΡΡ
, Π° ΠΈΡ
ΡΡΠ΅Π΄Π½Π΅Π΅ Π°ΡΠΈΡΠΌΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΡΠ΅Π½ΠΊΠΎΠΉ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π²ΡΠ΅Π³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ. ΠΡΠ»ΠΈ Π½Π° ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΈ Π΄ΠΎΠΌΠΈΠ½ΠΈΡΡΡΡ Π±ΠΎΠ»ΡΡΠΈΠ΅ ΠΎΠ±Π»Π°ΡΡΠΈ ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΠΎΠΉ ΡΡΠΊΠΎΡΡΠΈ, ΠΌΠ΅ΡΡ ΡΠ°ΠΊΠΎΠ³ΠΎ ΡΠΈΠΏΠ° ΠΌΠΎΠ³ΡΡ Π΄Π°ΡΡ Π½Π΅Π²Π΅ΡΠ½ΡΠ΅ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π°, Π½Π΅ ΡΠΎΠ²ΠΏΠ°Π΄Π°ΡΡΠΈΠ΅ Ρ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΠΎΡΠ΅Π½ΠΊΠ°ΠΌΠΈ
ΠΠΠ ΠΠΠΠ’Π Π« ΠΠ ΠΠΠΠ Π ΠΠ‘ΠΠ ΠΠΠΠΠΠΠΠ― ΠΠΠΠΠΠ¬ΠΠ«Π₯ ΠΠ¦ΠΠΠΠ ΠΠΠ ΠΠΠ Π« ΠΠΠ§ΠΠ‘Π’ΠΠ ΠΠΠΠΠ ΠΠΠΠΠΠ
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 ΡΡΠ°ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° Π²ΡΠ±ΠΎΡΠ° Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΈΠ· ΡΠ΅ΡΠΈΠΈ Π² ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ ΡΡΠ°Π»ΠΎΠ½Π°. ΠΠΏΠΈΡΡΠ²Π°ΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΊ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π½Π° ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΠΈ ΠΊΡΠΈΠ²ΠΎΠΉ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ ΠΊΠ°ΡΠ΅ΡΡΠ²Π°. ΠΠ΄ΠΈΠ½ ΠΈΠ· ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΠ°ΠΊΠΎΠΉ ΠΊΡΠΈΠ²ΠΎΠΉ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ ΠΊΠ°ΠΊ ΠΌΠ΅ΡΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ. ΠΡΠΎΠ±ΡΠ°Π½Ρ 16 ΠΌΠ΅Ρ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΎΠΏΠΈΡΠ°Π½Π½ΡΡ
Π² Π½Π°ΡΡΠ½ΠΎΠΉ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ΅. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡ ΠΌΠ°ΡΡΡΠ°Π±Π° ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΠ΅ΠΉΠ±ΡΠ»Π»Π° ΡΠ²Π»ΡΠ΅ΡΡΡ Π±ΠΎΠ»Π΅Π΅ ΡΠΎΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅Π³ΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΌΠ΅ΡΠΎΠΉ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π΄Π»Ρ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ, ΡΠ΅ΠΌ ΡΡΠ΅Π΄Π½Π΅Π΅ Π°ΡΠΈΡΠΌΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅. ΠΡΠΏΠΎΠ»Π½Π΅Π½ ΡΡΠ΄ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ², ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡΠΈΡ
ΠΊΠΎΡΡΠ΅ΠΊΡΠ½ΠΎΡΡΡ ΡΠ°ΠΊΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈ Π΅Π΅ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ Ρ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΠΎΡΠ΅Π½ΠΊΠ°ΠΌΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ. ΠΠ°Π»ΠΈΡΠΈΠ΅ ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΡ
ΠΌΠ΅Ρ ΠΎΡΠ΅Π½Ρ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎ Π΄Π»Ρ Π°) ΠΎΡΠ΅Π½ΠΊΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈ ΡΠΎΡΠΌΠΈΡΡΠ΅ΠΌΡΡ
ΡΠΎΡΠΎΠ³ΡΠ°ΡΠΈΠΉ, Π±) Π²ΡΠ±ΠΎΡΠ° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ, ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ ΡΡΠΊΠΎΡΡΠ½ΡΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ, ΡΠΆΠ°ΡΠΈΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° ΡΡΠΊΠΎΡΡΠΈ, ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ Π² ΠΏΠΎΠ»ΡΡΠΎΠ½ΠΎΠ²ΠΎΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΠΈ Π΄ΡΡΠ³ΠΈΡ
Π Π¦ΠΠ€Π ΠΠΠΠ Π ΠΠ‘Π’ΠΠΠ ΠΠ¦ΠΠ ΠΠ‘Π’ΠΠ ΠΠ§ΠΠ‘ΠΠΠ₯ Π’ΠΠΠ‘Π’ΠΠΠ«Π₯ ΠΠΠΠ£ΠΠΠΠ’ΠΠ
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°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠ΅ΡΡΠ°Π²ΡΠ°ΡΠΈΠΈ ΡΡΠ°ΡΡΡ
ΡΠ΅ΠΊΡΡΠΎΠ²ΡΡ
Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ² ΠΈ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ ΠΊ ΠΈΡ
ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΡΠΈ ΡΠ΅ΡΡΠ°Π²ΡΠ°ΡΠΈΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΡΠΈΡ
Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ² ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ. ΠΠΎΠΊΡΠΌΠ΅Π½ΡΡ-ΠΏΠ΅ΡΠ²ΠΎΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈ ΠΏΡΠΈ ΡΡΠΎΠΌ Π½Π΅ ΠΏΠΎΠ΄Π²Π΅ΡΠ³Π°ΡΡΡΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡΠΌ, Π° ΠΈΡ
ΡΠΈΡΡΠΎΠ²ΡΠ΅ ΠΊΠΎΠΏΠΈΠΈ ΠΌΠΎΠΆΠ½ΠΎ ΠΌΠΎΠ΄ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°ΡΡ Ρ ΠΎΡΠΈΠ΅Π½ΡΠ°ΡΠΈΠ΅ΠΉ Π½Π° ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΈ ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ ΡΠ°Π·Π½ΡΠΌ ΡΡΠΎΠ²Π½ΡΠΌ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ.
Application of the Clahe Method Contrast Enhancement of X-Ray Images
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
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‘ΠΠΠΠ£ΠΠ―Π ΠΠΠ Π ΠΠΠΠΠΠΠΠΠ ΠΠΠ’Π ΠΠ¦ Π ΠΠΠΠΠΠΠ Π¦ΠΠ€Π ΠΠΠ«Π₯ ΠΠΠΠΠ ΠΠΠΠΠΠ
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.ΠΠΏΠΈΡΡΠ²Π°ΡΡΡΡ Π½ΠΎΠ²ΡΠ΅ ΡΠ²ΠΎΠΉΡΡΠ²Π° ΡΠΈΠ½Π³ΡΠ»ΡΡΠ½ΡΡ
ΡΠΈΡΠ΅Π», Π²ΡΡΠΈΡΠ»ΡΠ΅ΠΌΡΡ
Π΄Π»Ρ ΠΌΠ°ΡΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΈΠ·ΠΎ-Π±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΠΏΠ΅ΡΠ΅ΡΡΠ°Π½ΠΎΠ²ΠΊΠ° ΡΡΡΠΎΠΊ ΠΈΠ»ΠΈ ΡΡΠΎΠ»Π±ΡΠΎΠ² ΠΌΠ°ΡΡΠΈΡΡ ΠΈ Π΅Π΅ ΠΏΠΎΠ²ΠΎΡΠΎΡ Π½Π° 90Β° Π½Π΅ ΠΌΠ΅Π½ΡΡΡ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΠΈΠ½Π³ΡΠ»ΡΡΠ½ΡΡ
ΡΠΈΡΠ΅Π», ΠΎΠ΄Π½Π°ΠΊΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ° ΠΈΠ»ΠΈ ΠΏΠ΅ΡΠ΅ΡΡΠ°Π½ΠΎΠ²ΠΊΠ° ΠΌΠ΅ΡΡΠ°ΠΌΠΈ Π΄Π²ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² ΠΌΠ°ΡΡΠΈΡΡ ΠΌΠΎΠ³ΡΡ ΠΏΡΠΈΠ²Π΅ΡΡΠΈ ΠΊ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π²ΡΠ΅Π³ΠΎ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΠΈΠ½Π³ΡΠ»ΡΡΠ½ΡΡ
ΡΠΈΡΠ΅Π». ΠΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ ΠΏΡΠΈΠΌΠ΅ΡΡ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΠ΅Π·ΠΊΠΎΡΡΠΈ ΠΈ ΠΊΠΎΠ½ΡΡΠ°ΡΡΠ° ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΡΡΠ΅ΠΌ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΠΈΠ½Π³ΡΠ»ΡΡΠ½ΡΡ
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